Explain precision, recall, and F1 score.

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Precision, Recall, and F1 Score are key metrics used to evaluate classification models, especially when dealing with imbalanced datasets where accuracy alone is misleading.

• Precision (Positive Predictive Value):
  It measures how many of the predicted positives are actually correct.
  Formula: Precision = TP / (TP + FP)
  High precision means fewer false positives. Example: If a spam filter flags 100 emails as spam and 90 are truly spam, precision = 90%.

• Recall (Sensitivity or True Positive Rate):
  It measures how many of the actual positives the model correctly identifies.
  Formula: Recall = TP / (TP + FN)
  High recall means fewer false negatives. Example: If there are 100 spam emails and the model detects 80, recall = 80%.

• F1 Score:
  It is the harmonic mean of precision and recall, providing a balance between them.
  Formula: F1 = 2 × (Precision × Recall) / (Precision + Recall)
  It is useful when both false positives and false negatives matter.

Usage in AI:

• Precision is crucial when false positives are costly (e.g., fraud detection).

• Recall is important when missing positives is risky (e.g., cancer detection).

• F1 score is preferred when we need a trade-off.

In short, precision, recall, and F1 score provide deeper insight into model performance beyond accuracy.

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